Data Visualization with R and ggplot

First go here:

http://github.com/rrlab/ggplot-lecture}{github.com/rrlab/ggplot-lecture

Download zip file

Then install the following packages:

install.packages("ggplot2", repos="http://cran.rstudio.com/")
## 
## The downloaded binary packages are in
##  /var/folders/g5/s26yfnqs4dj12pn6w88g__cc0000gn/T//Rtmp4Rmq2o/downloaded_packages
install.packages("dplyr", repos="http://cran.rstudio.com/")
## 
## The downloaded binary packages are in
##  /var/folders/g5/s26yfnqs4dj12pn6w88g__cc0000gn/T//Rtmp4Rmq2o/downloaded_packages
install.packages("tidyr", repos="http://cran.rstudio.com/")
## 
## The downloaded binary packages are in
##  /var/folders/g5/s26yfnqs4dj12pn6w88g__cc0000gn/T//Rtmp4Rmq2o/downloaded_packages
install.packages("magrittr", repos="http://cran.rstudio.com/")
## 
## The downloaded binary packages are in
##  /var/folders/g5/s26yfnqs4dj12pn6w88g__cc0000gn/T//Rtmp4Rmq2o/downloaded_packages
install.packages("RColorBrewer", repos="http://cran.rstudio.com/")
## 
## The downloaded binary packages are in
##  /var/folders/g5/s26yfnqs4dj12pn6w88g__cc0000gn/T//Rtmp4Rmq2o/downloaded_packages

And then load them:

library("ggplot2")
library("dplyr")
## 
## Attaching package: 'dplyr'
## 
## The following object is masked from 'package:stats':
## 
##     filter
## 
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library("tidyr")
library("magrittr")
## 
## Attaching package: 'magrittr'
## 
## The following object is masked from 'package:tidyr':
## 
##     extract
library("RColorBrewer")

Base graphics

ggplot2

Why ggplot2?

Basics

Some terminology

  • ggplot The main function where you specify the dataset and variables to plot

  • geoms geometric objects

  • geom_point(), geom_bar(), geom_density(), geom_line(), geom_area()

  • aes aesthetics

  • shape, transparency (alpha), color, fill, linetype.

  • scales Define how your data will be plotted -continuous, discrete, log

Assembling your first ggplot

The iris dataset

head(iris)
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa
tail(iris)
##     Sepal.Length Sepal.Width Petal.Length Petal.Width   Species
## 145          6.7         3.3          5.7         2.5 virginica
## 146          6.7         3.0          5.2         2.3 virginica
## 147          6.3         2.5          5.0         1.9 virginica
## 148          6.5         3.0          5.2         2.0 virginica
## 149          6.2         3.4          5.4         2.3 virginica
## 150          5.9         3.0          5.1         1.8 virginica
glimpse(iris)
## Observations: 150
## Variables:
## $ Sepal.Length (dbl) 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9,...
## $ Sepal.Width  (dbl) 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1,...
## $ Petal.Length (dbl) 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5,...
## $ Petal.Width  (dbl) 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1,...
## $ Species      (fctr) setosa, setosa, setosa, setosa, setosa, setosa, ...

Let’s try an example

ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_point()

Basic structure

ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width)) + 
  geom_point()

myplot <- ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width))
myplot + geom_point()

  • Specify the data and variables inside the ggplot function.
  • Anything else that goes in here becomes a global setting.
  • Then add layers of geometric objects, statistical models, and panels.

Alternative modes to say the same thing

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_point()

iris %>%
ggplot(aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_point()

Quick notes

  • Never use - short for quick plot.
  • You’ll end up unlearning and relearning a good bit.
  • Check the name of your variables Sepal.length sepal.length SepalLength
  • Be consistent with your variable naming. I like sepal_length
  • Have you closed all your parenthesis? This:

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width) + geom_point()

won´t work.

Let´s do some modifications

Increase the size of points

ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_point(size = 3)

Add some color

ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_point(size = 3)

Differentiate points by shape

ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_point(aes(shape = Species), size = 3)

Exercise 1

Make a small sample of the diamonds dataset

d2 <- diamonds[sample(1:dim(diamonds)[1], 1000), ]

Then generate the graph below:

Box plots

ggplot(iris, aes(Species, Sepal.Length)) + 
  geom_boxplot()

What if you also want to see all the individual points?

This is where the ggplot excels.

ggplot(iris, aes(Species, Sepal.Length)) +
geom_boxplot() +
geom_point(position = "jitter")

Histograms

h <- ggplot(faithful, aes(x = waiting))
h + geom_histogram(binwidth = 30, colour = "black")

h + geom_histogram(binwidth = 8, fill = "steelblue",
colour = "black")

Line plots

climate <- read.csv("climate.csv", header = T)
ggplot(climate, aes(Year, Anomaly10y)) +
geom_line()

We can also plot confidence regions

ggplot(climate, aes(Year, Anomaly10y)) +
  geom_ribbon(aes(ymin = Anomaly10y - Unc10y,
                  ymax = Anomaly10y + Unc10y),
              fill = "blue", alpha = .1) +
  geom_line(color = "steelblue")

Bar plots

ggplot(iris, aes(Species, Sepal.Length)) +
geom_bar(stat = "identity")

tidyr and dplyr are key for preparing data into ggplot friendly format.

head(iris)
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa
df <- gather(iris, organ, value, -Species)
head(iris)
##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa
ggplot(df, aes(Species, value, fill = organ)) +
geom_bar(stat = "identity", position = "dodge")

Exercise

Using the d2 dataset you created earlier, generate this plot below. Take a quick look at the data first to see if it needs to be binned.

Exercise

Using the climate dataset, create a new variable called sign. Make it logical (true/false) based on the sign of Anomaly10y.

## Warning in loop_apply(n, do.ply): Stacking not well defined when ymin != 0

Density Plots

ggplot(faithful, aes(waiting)) + 
  geom_density()

some polishing

ggplot(faithful, aes(waiting)) +
  geom_density(fill = "blue", alpha = 0.1)

another way to do the same

ggplot(faithful, aes(waiting)) +
  geom_line(stat = "density")

Mapping Variables to colors

Different ways to specify colors:

Or add it as a scale

The RColorBrewer package

display.brewer.all()

Using a color brewer palette

df  <- gather(iris, organ, measurement, -Species)
ggplot(df, aes(Species, measurement, fill = organ)) +
geom_bar(stat = "identity", position = "dodge") +
scale_fill_brewer(palette = "Set1")

Manual color scale

ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_point() +
  scale_color_manual(values = c("red", "green", "blue"))

Refer to a color chart for beautiful visualizations

I want hue

I want hue

Faceting

Facets allow you to keep your graphs clean and easy to read. Very useful in time series data.

Faceting along columns

ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
geom_point() +
facet_grid(Species ~ .)

and along rows

ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_point() +
  facet_grid(. ~ Species)

Adding smoothers

ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_point(aes(shape = Species), size = 3) +
  geom_smooth(method = "lm")

here we have a typical example where facetting will be usefull

ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_point(size = 3) +
  geom_smooth(method = "lm") +
  facet_grid(. ~ Species)

Themes

Themes are a great way to define custom plots

A themed plot

ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_point(size = 1.2, shape = 16) +
  facet_wrap( ~ Species) +
  theme(legend.key = element_rect(fill = NA),
        legend.position = "bottom",
        strip.background = element_rect(fill = NA),
        axis.title.y = element_text(angle = 0))

ggthemes library

install.packages('ggthemes', repos="http://cran.rstudio.com/")
## 
## The downloaded binary packages are in
##  /var/folders/g5/s26yfnqs4dj12pn6w88g__cc0000gn/T//Rtmp4Rmq2o/downloaded_packages
library(ggthemes)

plot <- ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
  geom_point(size = 1.2, shape = 16) +
  facet_wrap( ~ Species)

# Then add one of these themes to your plot

plot + theme_stata()

plot + theme_excel()

plot + theme_wsj()

plot + theme_solarized()

plot + theme_gdocs()

plot + theme_economist()

Create functions to automate your plotting

my_custom_plot <- function(df, title = “”, …) { ggplot(df, …) + ggtitle(title) + whatever_geoms() + theme(…)

Then just call your function to generate a plot. It’s a lot easier to fix one function that do it over and over for many plots

plot1 <- my_custom_plot(dataset1, title = “Figure 1”)

Scales

scale_fill_discrete(); scale_colour_discrete()
## discrete_scale(aesthetics = "fill", scale_name = "hue", palette = hue_pal(h, 
##     c, l, h.start, direction), na.value = na.value)
## discrete_scale(aesthetics = "colour", scale_name = "hue", palette = hue_pal(h, 
##     c, l, h.start, direction), na.value = na.value)
scale_fill_hue(); scale_color_hue()
## discrete_scale(aesthetics = "fill", scale_name = "hue", palette = hue_pal(h, 
##     c, l, h.start, direction), na.value = na.value)
## discrete_scale(aesthetics = "colour", scale_name = "hue", palette = hue_pal(h, 
##     c, l, h.start, direction), na.value = na.value)
scale_fill_manual();  scale_color_manual()
## discrete_scale(aesthetics = aesthetic, scale_name = "manual", 
##     palette = pal)
## discrete_scale(aesthetics = aesthetic, scale_name = "manual", 
##     palette = pal)
scale_fill_brewer(); scale_color_brewer()
## discrete_scale(aesthetics = "fill", scale_name = "brewer", palette = brewer_pal(type, 
##     palette))
## discrete_scale(aesthetics = "colour", scale_name = "brewer", 
##     palette = brewer_pal(type, palette))
scale_linetype(); scale_shape_manual()
## discrete_scale(aesthetics = "linetype", scale_name = "linetype_d", 
##     palette = linetype_pal(), na.value = na.value)
## discrete_scale(aesthetics = aesthetic, scale_name = "manual", 
##     palette = pal)

Gradients

h + geom_histogram( aes(fill = ..count..), color="black") +
scale_fill_gradient(low="green", high="red")

Publication quality figures

If the plot is in your screen:

ggsave('filename.png')
## Saving 7 x 5 in image

If the plot is assigned to an object

plot1 <-ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width)) +
  geom_point()
ggsave(plot1, file = "filename.png")
## Saving 7 x 5 in image

Specify a size

ggsave(file = "filename.png", width = 6, height =4) # default units in inches

Or any format (pdf, png, eps, svg, jpg)

ggsave(file = "filename.eps")
## Saving 7 x 5 in image
ggsave(file = "filename.jpg")
## Saving 7 x 5 in image
ggsave(file = "filename.pdf") # preferred format for final publication images
## Saving 7 x 5 in image

Further help